{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,23]],"date-time":"2026-02-23T13:43:11Z","timestamp":1771854191344,"version":"3.50.1"},"reference-count":8,"publisher":"World Scientific Pub Co Pte Ltd","issue":"01","funder":[{"name":"National Science Foundation","award":["1639750"],"award-info":[{"award-number":["1639750"]}]},{"name":"National Science Foundation","award":["1639720"],"award-info":[{"award-number":["1639720"]}]},{"name":"National Science Foundation","award":["1639640"],"award-info":[{"award-number":["1639640"]}]},{"name":"National Science Foundation","award":["1639570"],"award-info":[{"award-number":["1639570"]}]},{"name":"National Science Foundation","award":["1639554"],"award-info":[{"award-number":["1639554"]}]},{"name":"National Science Foundation","award":["1740704"],"award-info":[{"award-number":["1740704"]}]},{"name":"National Science Foundation","award":["1740627"],"award-info":[{"award-number":["1740627"]}]},{"name":"National Science Foundation","award":["1740696"],"award-info":[{"award-number":["1740696"]}]},{"name":"CAREER award Stamps","award":["1943681"],"award-info":[{"award-number":["1943681"]}]},{"name":"the National Geographic Society to Stamps","award":["NGS CP-730R-17"],"award-info":[{"award-number":["NGS CP-730R-17"]}]},{"name":"National Science Foundation and the National Aeronautics and Space Administration under NSF Cooperative Agreement","award":["EAR-1724794"],"award-info":[{"award-number":["EAR-1724794"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Semantic Computing"],"published-print":{"date-parts":[[2022,3]]},"abstract":"<jats:p> Artificial intelligence applications within the geosciences are becoming increasingly common, yet there are still many challenges involved in adapting established techniques to geoscience data sets. Applications in the realm of volcanic hazards assessment show great promise for addressing such challenges. Here, we describe a Jupyter Notebook we developed that ingests real-time Global Navigation Satellite System (GNSS) data streams from the EarthCube CHORDS (Cloud-Hosted Real-time Data Services for the geosciences) portal TZVOLCANO, applies unsupervised learning algorithms to perform automated data quality control (\u201cnoise reduction\u201d), and explores autonomous detection of unusual volcanic activity using a neural network. The TZVOLCANO CHORDS portal streams real-time GNSS positioning data in 1[Formula: see text]s intervals from the TZVOLCANO network, which monitors the active volcano Ol Doinyo Lengai in Tanzania, through UNAVCO\u2019s real-time GNSS data services. UNAVCO\u2019s real-time data services provide near-real-time positions processed by the Trimble Pivot system. The positioning data (latitude, longitude and height) are imported into the Jupyter Notebook presented in this paper in user-defined time spans. The positioning data are then collected in sets by the Jupyter Notebook and processed to extract a useful calculated variable in preparation for the machine learning algorithms, of which we choose the vector magnitude for further processing. Unsupervised K-means and Gaussian Mixture machine learning algorithms are then utilized to locate and remove data points (\u201cfilter\u201d) that are likely caused by noise and unrelated to volcanic signals. We find that both the K-means and Gaussian Mixture machine learning algorithms perform well at identifying regions of high noise within tested GNSS data sets. The filtered data are then used to train an artificial intelligence neural network that predicts volcanic deformation. Our Jupyter Notebook has promise to be used for detecting potentially hazardous volcanic activity in the form of rapid vertical or horizontal displacement of the Earth\u2019s surface. <\/jats:p>","DOI":"10.1142\/s1793351x22400025","type":"journal-article","created":{"date-parts":[[2022,4,5]],"date-time":"2022-04-05T06:46:21Z","timestamp":1649141181000},"page":"29-45","source":"Crossref","is-referenced-by-count":4,"title":["Toward Autonomous Detection of Anomalous GNSS Data Via Applied Unsupervised Artificial Intelligence"],"prefix":"10.1142","volume":"16","author":[{"given":"Mike","family":"Dye","sequence":"first","affiliation":[{"name":"Ronin Institute, 127 Haddon Pl, Montclair, New Jersey 07043, USA"},{"name":"P.O. Box 56, Nederland, Colorado 80466, USA"}]},{"given":"D. Sarah","family":"Stamps","sequence":"additional","affiliation":[{"name":"Department of Geosciences, Virginia Tech, 926 W. Campus Drive Blacksburg, Virginia 24061, USA"}]},{"given":"Myles","family":"Mason","sequence":"additional","affiliation":[{"name":"Academy of Integrated Science, Virginia Tech, 800 W. Campus Drive, Blacksburg, Virginia 24061, USA"}]},{"given":"Elifuraha","family":"Saria","sequence":"additional","affiliation":[{"name":"Department of Geospatial Sciences and Technology, Ardhi University, P.O. Box 35176, Observation Hill, Plot No. 3, Block L, University Road, Dar es Salaam, Tanzania"}]}],"member":"219","published-online":{"date-parts":[[2022,4,4]]},"reference":[{"key":"S1793351X22400025BIB002","doi-asserted-by":"publisher","DOI":"10.1029\/2021EO160257"},{"key":"S1793351X22400025BIB003","doi-asserted-by":"publisher","DOI":"10.1007\/3-540-46805-6_19"},{"key":"S1793351X22400025BIB004","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/D14-1179"},{"issue":"1","key":"S1793351X22400025BIB006","first-page":"1929","volume":"15","author":"Srivastava N.","year":"2014","journal-title":"J. Mach. Learn. Res."},{"key":"S1793351X22400025BIB016","doi-asserted-by":"publisher","DOI":"10.1002\/gdj3.36"},{"key":"S1793351X22400025BIB020","volume-title":"Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems","author":"G\u00e9ron A.","year":"2019","edition":"2"},{"key":"S1793351X22400025BIB021","first-page":"1460","volume-title":"Proc. 24th Int. Technical Meeting of the Satellite Division of the Institute of Navigation","author":"Leandro R.","year":"2011"},{"key":"S1793351X22400025BIB023","first-page":"153","volume":"3","author":"Scheffer J.","year":"2002","journal-title":"Res. Lett. Inf. Math. Sci."}],"container-title":["International Journal of Semantic Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.worldscientific.com\/doi\/pdf\/10.1142\/S1793351X22400025","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,4,26]],"date-time":"2022-04-26T08:42:06Z","timestamp":1650962526000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.worldscientific.com\/doi\/10.1142\/S1793351X22400025"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3]]},"references-count":8,"journal-issue":{"issue":"01","published-print":{"date-parts":[[2022,3]]}},"alternative-id":["10.1142\/S1793351X22400025"],"URL":"https:\/\/doi.org\/10.1142\/s1793351x22400025","relation":{},"ISSN":["1793-351X","1793-7108"],"issn-type":[{"value":"1793-351X","type":"print"},{"value":"1793-7108","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,3]]}}}